Spatiotemporal changes, faulting regimes, and source parameters of induced seismicity: A case study from The Geysers geothermal field
Bibliographic record
Abstract
Abstract The spatiotemporal, kinematic, and source characteristics of induced seismicity occurring at different fluid injection rates are investigated to determine the predominant physical mechanisms responsible for induced seismicity at the northwestern part of The Geysers geothermal field, California. We analyze a relocated hypocenter catalog from a seismicity cluster where significant variations of the stress tensor orientation were previously observed to correlate with injection rates. We find that these stress tensor orientation changes may be related to increased pore pressure and the corresponding changes in poroelastic stresses at reservoir depth. Seismic events during peak injections tend to occur at greater distances from the injection well, preferentially trending parallel to the maximum horizontal stress direction. In contrast, at lower injection rates the seismicity tends to align in a different direction which suggests the presence of a local fault. During peak injection intervals, the relative contribution of strike‐slip faulting mechanisms increases. Furthermore, increases in fluid injection rates also coincide with a decrease in b values. Our observations suggest that regardless of the injection stage, most of the induced seismicity results from thermal fracturing of the reservoir rock. However, during peak injection intervals, the increase in pore pressure may likewise be responsible for the induced seismicity. By estimating the thermal and hydraulic diffusivities of the reservoir, we confirm that the characteristic diffusion length for pore pressure is much greater than the corresponding length scale for temperature and also more consistent with the spatial extent of seismicity observed during different injection rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".